CONTINUOUS LEARNING FOR OBJECT TRACKING

    公开(公告)号:US20210312642A1

    公开(公告)日:2021-10-07

    申请号:US17057084

    申请日:2019-01-03

    Abstract: A long-term object tracker employs a continuous learning framework to overcome drift in the tracking position of a tracked object. The continuous learning framework consists of a continuous learning module that accumulates samples of the tracked object to improve the accuracy of object tracking over extended periods of time. The continuous learning module can include a sample pre-processor to refine a location of a candidate object found during object tracking, and a cropper to crop a portion of a frame containing a tracked object as a sample and to insert the sample into a continuous learning database to support future tracking.

    METHODS AND APPARATUS TO MATCH IMAGES USING SEMANTIC FEATURES

    公开(公告)号:US20210174134A1

    公开(公告)日:2021-06-10

    申请号:US16768559

    申请日:2018-03-01

    Abstract: Methods and apparatus to match images using semantic features are disclosed. An example apparatus includes a semantic labeler to determine a semantic label for each of a first set of points of a first image and each of a second set of points of a second image; a binary robust independent element features (BRIEF) determiner to determine semantic BRIEF descriptors for a first subset of the first set of points and a second subset of the second set of points based on the semantic labels; and a point matcher to match first points of the first subset of points to second points of the second subset of points based on the semantic BRIEF descriptors.

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